64 research outputs found
Detecting Robotic Anomalies using RobotChain
Robotic events can provide notable amounts of
information regarding a robot’s status, which can be extrapolated
to detect productivity, anomalies, malfunctions and used
for monitorization. However, when problems occur in sensitive
environments like a factory, the logs of a machine may be
discarded because they are susceptible to chances and malicious
intents. In this paper we propose to use RobotChain for anomaly
detection. RobotChain is a method to securely register robotic
events, using a blockchain, which ensures that once an event
gets registered on it, it’s secured and cannot be tampered with.
We show how this system can be leveraged with the module for
anomaly detection, that uses the information contained on the
blockchain to detect anomalies on a UR3 robot.This work was partially supported by the Tezos Fundation through a grant for project Robotchaininfo:eu-repo/semantics/publishedVersio
RobotChain: Artificial Intelligence on a Blockchain using Tezos Technology
Blockchain technology is not only growing everyday at a fast-passed rhythm, but it is also a disruptive technology that has changed how we look at financial transactions. By providing a way
to trust an unknown network and by allowing us to conduct transactions without the need for a
central authority, blockchain has grown exponentially. Moreover, blockchain also provides decentralization of the data, immutability, accessibility, non-repudiation and irreversibility properties that makes this technology a must in many industries. But, even thought blockchain
provides interesting properties, it has not been extensively used outside the financial scope.
Similarly, robots have been increasingly used in factories to automate tasks that range from
picking objects, to transporting them and also to work collaboratively with humans to perform
complex tasks. It is important to enforce that robots act between legal and moral boundaries
and that their events and data are securely stored and auditable. This rarely happens, as robots
are programmed to do a specific task without certainty that that task will always be performed
correctly and their data is either locally stored, without security measures, or disregarded. This
means that the data, especially logs, can be altered, which means that robots and manufacturers can be accused of problems that they did not cause. Henceforth, in this work, we sought
to integrate blockchain with robotics with the goal to provide enhanced security to robots, to
the data and to leverage artificial intelligence algorithms. By doing an extensive overview of
the methods that integrate blockchain and artificial intelligence or robotics, we found that this
is a growing field but there is a lack of proposals that try to improve robotic systems by using
blockchain. It was also clear that most of the existing proposals that integrate artificial intelligence and blockchain, are focused on building marketplaces and only use the latter to storage
transactions. So, in this document, we proposed three different methods that use blockchain
to solve different problems associated with robots. The first one is a method to securely store
robot logs in a blockchain by using smart-contracts as storage and automatically detect when
anomalies occur in a robot by using the data contained in the blockchain and a smart-contract.
By using smart-contracts, it is assured that the data is secure and immutable as long as the
blockchain has enough peers to participate in the consensus process. The second method goes
beyond registering events to also register information about external sensors, like a camera,
and by using smart-contracts to allow Oracles to interact with the blockchain, it was possible to
leverage image analysis algorithms that can detect the presence of material to be picked. This
information is then inserted into a smart-contract that automatically defines the movement that
a robot should have, regarding the number of materials present to be picked. The third proposal
is a method that uses blockchain to store information about the robots and the images derived
from a Kinect. This information is then used by Oracles that check if there is any person located
inside a robot workspace. If there is any, this information is stored and different Oracles try to
identify the person. Then, a smart-contract acts appropriately by changing or even stopping the
robot depending on the identity of the person and if the person is located inside the warning or
the critical zone surrounding the robot.
With this work, we show how blockchain can be used in robotic environments and how it
can beneficial in contexts where multi-party cooperation, security, and decentralization of the
data is essential. We also show how Oracles can interact with the blockchain and distributively
cooperate to leverage artificial intelligence algorithms to perform analysis in the data that
allow us to detect robotic anomalies, material in images and the presence of people. We also show that smart-contracts can be used to perform more tasks than just serve the purpose of
automatically do monetary transactions. The proposed architectures are modular and can be
used in multiple contexts such as in manufacturing, network control, robot control, and others
since they are easy to integrate, adapt, maintain and extend to new domains. We expect
that the intersection of blockchain and robotics will shape part of the future of robotics once
blockchain is more widely used and easy to integrate. This integration will be very prominent
in tasks where robots need to behave under certain constraints, in swarm robotics due to the
fact that blockchain offers global information and in factories because the actions undertaken
by a robot can easily be extended to the rest of the robots by using smart-contracts.Hoje em dia é possível ver que a blockchain não está apenas a crescer a um ritmo exponencial, mas que é também uma tecnologia disruptiva que mudou a forma como trabalhamos com
transações financeiras. Ao fornecer uma maneira eficiente de confiar numa rede desconhecida
e de permitir realizar transações sem a necessidade de uma autoridade central, a blockchain
cresceu rapidamente. Além disso, a blockchain fornece também descentralização de dados,
imutabilidade, acessibilidade, não-repúdio e irreversibilidade, o que torna esta tecnologia indispensável em muitos setores. Mas, mesmo fornecendo propriedades interessantes, a blockchain não tem sido amplamente utilizada fora do âmbito financeiro. Da mesma forma, os robôs
têm sido cada vez mais utilizados em fábricas para automatizar tarefas que vão desde pegar
objetos, transportá-los e colaborar com humanos para realizar tarefas complexas. Porém, é
importante impor que os robôs atuem entre certos limites legais e morais e que seus eventos
e dados são armazenados com segurança e que estes possam ser auditáveis. O problema é que
isso raramente acontece. Os robôs são programados para executar uma tarefa específica sem
se ter total certeza de que essa tarefa irá ser executada sempre de maneira correta, e os seus
dados são armazenados localmente, desconsiderando a segurança dos dados. Sendo que em
muitas ocasiões, não existe qualquer segurança. Isso significa que os dados, especialmente os
logs, podem ser alterados, o que pode resultar em que os robôs e, pela mesma linha de pensamento, os fabricantes, possam ser acusados de problemas que não causaram. Tendo isto em
consideração, neste trabalho, procuramos integrar a blockchain com a robótica, com o objetivo
de proporcionar maior segurança aos robôs e aos dados que geram e potenciar ainda a utilização de algoritmos de inteligência artificial. Fazendo uma visão abrangente dos métodos que
propõem integrar a blockchain e inteligência artificial ou robótica, descobrimos que este é um
campo em crescimento, mas que há uma falta de propostas que tentem melhorar os sistemas
robóticos utilizando a blockchain. Ficou também claro que a maioria das propostas existentes
que integram inteligência artificial e blockchain estão focadas na construção de marketplaces e
só utilizam a blockchain para armazenar a informação sobre as transações que foram executadas. Assim, neste documento, propomos três métodos que utilizam a blockchain para resolver
diferentes problemas associados a robôs. O primeiro é um método para armazenar, com segurança, logs de robôs dentro de uma blockchain, utilizando para isso smart-contracts como
armazenamento. Neste método foi também proposta uma maneira de detetar anomalias em
robôs automaticamente, utilizando para isso os dados contidos na blockchain e smart-contracts
para definir a lógica do algoritmo. Ao utilizar smart-contracts, é garantido que os dados são seguros e imutáveis, desde que a blockchain contenha nós suficientes a participar no algoritmo de
consenso. O segundo método vai além de registar eventos, para registar também informações
sobre sensores externos, como uma câmara, e utilizando smart-contracts para permitir que Óraculos interajam com a blockchain, foi possível utilizar algoritmos de análise de imagens, que
podem detetar a presença de material para ser recolhido. Esta informação é então inserida
num smart-contract que define automaticamente o movimento que um robô deve ter, tendo
em consideração a quantidade de material à espera para ser recolhida. A terceira proposta é
um método que utiliza a blockchain para armazenar informações sobre robôs, e imagens provenientes de uma Kinect. Esta informação é então utilizada por Óraculos que verificam se existe
alguma pessoa dentro do um espaço de trabalho de um robô. Se existir alguém, essa informação
é armazenada e diferentes Óraculos tentam identificar a pessoa. No fim, um smart-contract
age apropriadamente, mudando ou até mesmo parando o robô, dependendo da identidade da Com este trabalho, mostramos como a blockchain pode ser utilizada em ambientes onde existam robôs e como esta pode ser benéfica em contextos onde a cooperação entre várias entidades, a segurança e a descentralização dos dados são essenciais. Mostramos também como
Óraculos podem interagir com a blockchain e cooperar de forma distribuída, para alavancar
algoritmos de inteligência artificial de forma a realizar análises nos dados, o que nos permite
detetar anomalias robóticas, material para ser recolhido e a presença de pessoas em imagens.
Mostramos também que os smart-contracts podem ser utilizados para executar mais tarefas do
que servir o propósito de fazer transações monetárias de forma automática. As arquiteturas
propostas neste trabalho são modulares e podem ser utilizadas em vários contextos, como no
fabrico de peças, controle de robô e outras. Devido ao facto de que as arquiteturas propostas,
são fáceis de integrar, adaptar, manter e estender a novos domínios. A nossa opinião é que a
interseção entre a blockchain e a robótica irá moldar parte do futuro da robótica moderna assim
que a blockchain seja mais utilizada e fácil de integrar em sistemas robóticos. Esta integração
será muito proeminente em tarefas onde os robôs precisam de se comportar sob certas restrições, em enxames de robôs, devido ao fato de que a blockchain fornece informação global sobre
o estado da rede, e também em fábricas, porque as ações realizadas por um robô podem ser
facilmente estendidas ao resto dos robôs, e porque fornece um mecanismo extra de segurança
aos dados e a todas as ações que são efetuadas com ajuda de smart-contracts
A survey of modern exogenous fault detection and diagnosis methods for swarm robotics
Swarm robotic systems are heavily inspired by observations of social insects. This often leads to robust-ness being viewed as an inherent property of them. However, this has been shown to not always be thecase. Because of this, fault detection and diagnosis in swarm robotic systems is of the utmost importancefor ensuring the continued operation and success of the swarm. This paper provides an overview of recentwork in the field of exogenous fault detection and diagnosis in swarm robotics, focusing on the four areaswhere research is concentrated: immune system, data modelling, and blockchain-based fault detectionmethods and local-sensing based fault diagnosis methods. Each of these areas have significant advan-tages and disadvantages which are explored in detail. Though the work presented here represents a sig-nificant advancement in the field, there are still large areas that require further research. Specifically,further research is required in testing these methods on real robotic swarms, fault diagnosis methods,and integrating fault detection, diagnosis and recovery methods in order to create robust swarms thatcan be used for non-trivial tasks
Exogenous Fault Detection in Swarm Robotic Systems
Swarm robotic systems comprise many individual robots, and exhibit a degree of innate fault tolerance due to this built-in redundancy. They are robust in the sense that the complete failure of individual robots will have little detrimental effect on a swarm's overall collective behaviour. However, it has recently been shown that partially failed individuals may be harmful, and cause problems that cannot be solved by simply adding more robots to the swarm. Instead, an active approach to dealing with failed individuals is required for a swarm to continue operation in the face of partial failures. This thesis presents a novel method of exogenous fault detection that allows robots to detect the presence of faults in each other, via the comparison of expected and observed behaviour. Each robot predicts the expected behaviour of its neighbours by simulating them online in an internal replica of the real world. This expected behaviour is then compared against observations of their true behaviour, and any significant discrepancy is detected as a fault. This work represents the first step towards a distributed fault detection, diagnosis, and recovery process that would afford robot swarms a high degree of fault tolerance, and facilitate long-term autonomy
The suitability of the dendritic cell algorithm for robotic security applications
The implementation and running of physical security systems is costly and potentially hazardous for those employed to patrol areas of interest. From a technial perspective, the physical security problem can be seen as minimising the probability that intruders and other anomalous events will occur unobserved. A robotic solution is proposed using an artificial immune system, traditionally applied to software security, to identify threats and hazards: the dendritic cell algorithm. It is demonstrated that the migration from the software world to the hardware world is achievable for this algorithm and key properties of the resulting system are explored empirically and theoretically. It is found that the algorithm has a hitherto unknown frequency-dependent component, making it ideal for filtering out sensor noise. Weaknesses of the algorithm are also discovered, by mathematically phrasing the signal processing phase as a collection of linear classifiers. It is concluded that traditional machine learning approaches are likely to outperform the implemented system in its current form. However, it is also observed that the algorithm’s inherent filtering characteristics make modification, rather than rejection, the most beneficial course of action. Hybridising the dendritic cell algorithm with more traditional machine learning techniques, through the introduction of a training phase and using a non-linear classification phase is suggested as a possible future direction
A survey of modern exogenous fault detection and diagnosis methods for swarm robotics
Swarm robotic systems are heavily inspired by observations of social insects. This often leads to robust-ness being viewed as an inherent property of them. However, this has been shown to not always be thecase. Because of this, fault detection and diagnosis in swarm robotic systems is of the utmost importancefor ensuring the continued operation and success of the swarm. This paper provides an overview of recentwork in the field of exogenous fault detection and diagnosis in swarm robotics, focusing on the four areaswhere research is concentrated: immune system, data modelling, and blockchain-based fault detectionmethods and local-sensing based fault diagnosis methods. Each of these areas have significant advan-tages and disadvantages which are explored in detail. Though the work presented here represents a sig-nificant advancement in the field, there are still large areas that require further research. Specifically,further research is required in testing these methods on real robotic swarms, fault diagnosis methods,and integrating fault detection, diagnosis and recovery methods in order to create robust swarms thatcan be used for non-trivial tasks
The suitability of the dendritic cell algorithm for robotic security applications
The implementation and running of physical security systems is costly and potentially hazardous for those employed to patrol areas of interest. From a technial perspective, the physical security problem can be seen as minimising the probability that intruders and other anomalous events will occur unobserved. A robotic solution is proposed using an artificial immune system, traditionally applied to software security, to identify threats and hazards: the dendritic cell algorithm. It is demonstrated that the migration from the software world to the hardware world is achievable for this algorithm and key properties of the resulting system are explored empirically and theoretically. It is found that the algorithm has a hitherto unknown frequency-dependent component, making it ideal for filtering out sensor noise. Weaknesses of the algorithm are also discovered, by mathematically phrasing the signal processing phase as a collection of linear classifiers. It is concluded that traditional machine learning approaches are likely to outperform the implemented system in its current form. However, it is also observed that the algorithm’s inherent filtering characteristics make modification, rather than rejection, the most beneficial course of action. Hybridising the dendritic cell algorithm with more traditional machine learning techniques, through the introduction of a training phase and using a non-linear classification phase is suggested as a possible future direction
Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods
Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques.
The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns.
The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other.
The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques.
The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy
Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles
Unmanned aerial vehicles (UAVs) are rapidly becoming a critical military asset. In the future, advances in miniaturization are going to drive the development of insect size UAVs. New approaches to controlling these swarms are required. The goal of this research is to develop a controller to direct a swarm of UAVs in accomplishing a given mission. While previous efforts have largely been limited to a two-dimensional model, a three-dimensional model has been developed for this project. Models of UAV capabilities including sensors, actuators and communications are presented. Genetic programming uses the principles of Darwinian evolution to generate computer programs to solve problems. A genetic programming approach is used to evolve control programs for UAV swarms. Evolved controllers are compared with a hand-crafted solution using quantitative and qualitative methods. Visualization and statistical methods are used to analyze solutions. Results indicate that genetic programming is capable of producing effective solutions to multi-objective control problems
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